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Computed Tomography01:10

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Related Experiment Video

Updated: Jun 6, 2026

Clinical Imaging of Microwave Mammography
05:28

Clinical Imaging of Microwave Mammography

Published on: November 14, 2025

Grid infrastructures for developing mammography CAD systems.

Raul Ramos-Pollan1, Jose M Franco, Jorge Sevilla

  • 1Center of Extremadura for Advanced Technologies, CETA-CIEMAT, Spain. raul.ramos@ciemat.es

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary

This study developed Grid-based technologies for breast cancer computer-aided diagnosis (CAD), achieving high accuracy in classifying mammograms. These tools are now being validated in clinical settings.

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Area of Science:

  • Medical Imaging
  • Computer Science
  • Oncology

Background:

  • Breast cancer diagnosis relies on accurate interpretation of mammography images.
  • Existing diagnostic tools can be enhanced by computational analysis and machine learning.
  • Grid infrastructures offer potential for distributed data storage and processing.

Purpose of the Study:

  • To develop and evaluate Grid-based technologies for breast cancer computer-aided diagnosis (CAD).
  • To create federated repositories for mammography and clinical data.
  • To build and train machine learning classifiers for breast cancer detection using Grid computing power.

Main Methods:

  • Development of federated repositories for mammography images and clinical data.
  • Creation of a mammography image analysis workstation.
  • Implementation of a framework for data analysis and machine learning classifier training on Grid infrastructure.
  • Utilized MIAS database for mammograms and UCI Breast Cancer Wisconsin dataset.

Main Results:

  • Achieved an average area under the ROC curve of 0.85 for classifiers on a dataset of 100 mammograms.
  • Similar performance was obtained using the UCI Breast Cancer Wisconsin dataset.
  • Technologies are currently undergoing validation in a real medical environment at the Faculty of Medicine in Porto University.

Conclusions:

  • Grid infrastructures can be effectively leveraged for breast cancer CAD.
  • The developed technologies show promise for improving mammography image analysis and diagnosis.
  • Integration into clinical workflows is crucial for real-world application and validation.